# An Attribute-based Method for Video Anomaly Detection

arXiv: https://arxiv.org/abs/2212.00789 · HTML: https://mlsanity.com/paper/2212.00789/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2022.

## Built on by (most cited first, 21 of 21)

- [Advancing Video Anomaly Detection: A Concise Review and a New Dataset](https://mlsanity.com/paper/2402.04857/) (2024)
- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection](https://mlsanity.com/paper/2403.14497/) (2024)
- [An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction](https://mlsanity.com/paper/2406.15395/) (2024)
- [AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM](https://mlsanity.com/paper/2503.04504/) (2025)
- [A Unified Reasoning Framework for Holistic Zero-Shot Video Anomaly Analysis](https://mlsanity.com/paper/2511.00962/) (2025)
- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)
- [No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection](https://mlsanity.com/paper/2306.07284/) (2023)
- [Vision-Language Models Assisted Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2409.14109/) (2024)
- [Representation Learning in Anomaly Detection: Successes, Limits and a Grand Challenge](https://mlsanity.com/paper/2307.11085/) (2023)
- [Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approach](https://mlsanity.com/paper/2309.14622/) (2023)
- [Beyond the Benchmark: Detecting Diverse Anomalies in Videos](https://mlsanity.com/paper/2310.01904/) (2023)
- [uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories](https://mlsanity.com/paper/2404.12712/) (2024)
- [Real-Time Video Anomaly Detection Using YOLO Pose Estimation and CLIP-Based Semantic Scoring](https://mlsanity.com/paper/2608.31074/) (2026)
- [Probe-VAD: Ordinal Likelihood Probing for Training-Free Video Anomaly Detection](https://mlsanity.com/paper/2609.17211/) (2026)
- [Reasonable Anomaly Detection in Long Sequences](https://mlsanity.com/paper/2309.03401/) (2023)
- [Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection](https://mlsanity.com/paper/2403.19111/) (2024)
- [VelocityNet: Real-Time Crowd Anomaly Detection via Person-Specific Velocity Analysis](https://mlsanity.com/paper/2510.18187/) (2025)
- [GridVAD: Open-Set Video Anomaly Detection via Spatial Reasoning over Stratified Frame Grids](https://mlsanity.com/paper/2603.25467/) (2026)
- [Look Inside Each Video: Rethinking How Video Anomaly Detection Is Evaluated](https://mlsanity.com/paper/2608.21854/) (2026)
- [A Unified Score Matching Paradigm for Video Anomaly Detection and Anticipation](https://mlsanity.com/paper/2610.11149/) (2026)

## Abstract (arXiv)

Video anomaly detection (VAD) identifies suspicious events in videos, which is critical for crime prevention and homeland security. In this paper, we propose a simple but highly effective VAD method that relies on attribute-based representations. The base version of our method represents every object by its velocity and pose, and computes anomaly scores by density estimation. Surprisingly, this simple representation is sufficient to achieve state-of-the-art performance in ShanghaiTech, the most commonly used VAD dataset. Combining our attribute-based representations with an off-the-shelf, pretrained deep representation yields state-of-the-art performance with a 99.1%, 93.7%, and 85.9% AUROC on Ped2, Avenue, and ShanghaiTech, respectively.